• Team member EDUARD GIMENEZ
• Team member PATRICK ALTMEYER
• Team member SIMON NEUMEYER
• Team member JAKOB PÖRSCHMANN

• Data exploration
• Methodology
• Forecasts
• Interpretation of results

1. Fit classifier.fit(X,y) in parallel for all \(m \in [1,24]\):

\[ \begin{aligned} &&v_m&=\text{model}(\mathbf{X}_m) \\ \text{where}&& \mathbf{X}_m &= \begin{pmatrix} \mathbf{C}_1 & v_{1,-137} & ... & v_{1,m-1} \\ ... & ... & ... & ... \\ \mathbf{C}_{n_m} & v_{n_m,-137} & ... & v_{n_m,m-1} \end{pmatrix} \end{aligned} \]

1. Predict classifier.predict(X) recursively for all \(m \in [1,24]\) where

\[ \begin{aligned} && \mathbf{X}_m = \begin{pmatrix} \mathbf{C}_1 & v_{1,-137} & ... & \hat{v}_{1,m-1} \\ ... & ... & ... & ... \\ \mathbf{C}_{n_m} & v_{n_m,-137} & ... & \hat{v}_{n_m,m-1} \end{pmatrix} \end{aligned} \]

• Normalised predictions are (largely) \(\hat{\mathbf{v}}\in[0,1]\).
• We aim for small bands near 1 and 0 and wider bands elsewhere.
• Predictions can be thought of as being generated from a beta distribution with \(\mathbb{E}(\mathbf{v})=\frac{\alpha}{\alpha+\beta}\)
• Fix \(\beta=5\) and compute \(\alpha=\frac{\mathbf{v}\beta}{(1-\mathbf{v})}\) and then

L = sc.betaincinv(alpha, beta, .075)
U = sc.betaincinv(alpha, beta, .925)

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